1. CS162 Operating Systems and Systems Programming Lecture 23 Remote Procedure Call April 24, 2013 Anthony D. Joseph http://inst.eecs.berkeley.edu/~cs162

2. 23.2 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Goals for Today Remote Procedure Call Examples using RPC and caching –Distributed File Systems –World-Wide Web Note: Some slides and/or pictures in the following are adapted from slides ©2005 Silberschatz, Galvin, and Gagne. Many slides generated from my lecture notes by Kubiatowicz.

3. 23.3 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Remote Procedure Call Raw messaging is a bit too low-level for programming Another option: Remote Procedure Call (RPC) –Looks like a local procedure call on client: file.read(1024); –Translated automatically into a procedure call on remote machine (server) Implementation: –Uses request/response message passing “under the covers”

4. 23.4 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 RPC Details Client and server use “stubs” to glue pieces together –Client stub is responsible for “marshalling” arguments and “unmarshalling” the return values –Server-side stub is responsible for “unmarshalling” arguments and “marshalling” the return values Marshalling involves (depending on system) converting values to a canonical form, serializing objects, copying arguments passed by reference, etc. –Needs to account for cross-language and cross-platform issues Technique: compiler generated stubs –Input: interface definition language (IDL) »Contains, among other things, types of arguments/return –Output: stub code in the appropriate source language

5. 23.5 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 RPC Information Flow Client (caller) Server (callee) Packet Handler Packet Handler call return send receive send receive return call Network Client Stub bundle args bundle ret vals unbundle ret vals Server Stub unbundle args Machine A Machine B

6. 23.6 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 RPC Binding How does client know which machine to send RPC? –Need to translate name of remote service into network endpoint (e.g., host:port) –Binding: the process of converting a user-visible name into a network endpoint »This is another word for “naming” at network level »Static: fixed at compile time »Dynamic: performed at runtime Dynamic Binding –Most RPC systems use dynamic binding via name service –Why dynamic binding? »Access control: check who is permitted to access service »Fail-over: If server fails, use a different one

7. 23.7 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Cross-Domain Communication/Location Transparency How do address spaces communicate with one another? –Shared Memory with Semaphores, monitors, etc… –File System –Pipes (1-way communication) –“Remote” procedure call (2-way communication) RPC’s can be used to communicate between address spaces on different machines or the same machine –Services can be run wherever it’s most appropriate –Access to local and remote services looks the same Examples of modern RPC systems: –CORBA (Common Object Request Broker Architecture) –DCOM (Distributed COM) –RMI (Java Remote Method Invocation)

8. 23.8 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Microkernel Operating Systems Example: split kernel into application-level servers using RPC –File system looks remote, even though on same machine Why split the OS into separate domains? –Fault isolation: bugs are more isolated (build a firewall) –Enforces modularity: allows incremental upgrades of pieces of software (client or server) –Location transparent: service can be local or remote »For example in the X windowing system: Each X client can be on a separate machine from X server; Neither has to run on the machine with the frame buffer App file system Windowing Networking VM Threads App Monolithic Structure App File sys windows RPC address spaces threads Microkernel Structure

9. 23.9 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Problems with RPC Handling failures –Different failure modes in distributed system than on a single machine –Without RPC a failure within a procedure call usually meant whole application would crash/die –With RPC a failure within a procedure call means remote machine crashed, but local one could continue working –Answer? Distributed transactions can help Performance –Cost of Procedure call « same-machine RPC « network RPC –Means programmers must be aware they are using RPC (so much for transparency!) »Caching can help, but may make failure handling even more complex

10. 23.10 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Distributed File Systems Distributed File System: –Transparent access to files stored on a remote disk Naming choices (always an issue): –Hostname:localname: Name files explicitly »No location or migration transparency –Mounting of remote file systems »System manager mounts remote file system by giving name and local mount point »Transparent to user: all reads and writes look like local reads and writes to user e.g. /users/sue/foo  /sue/foo on server –A single, global name space: every file in the world has unique name »Location Transparency: servers can change and files can move without involving user Network Read File Data Client Server mount coeus:/sue mount adj:/prog mount adj:/jane

11. 23.11 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Simple Distributed File System EVERY read and write gets forwarded to server Advantage: Server provides completely consistent view of file system to multiple clients Problems? Performance! –Going over network is slower than going to local memory –Server can be a bottleneck Client Server Read (RPC) Return (Data) Client Write (RPC) ACK cache

12. 23.12 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Server cache F1:V1F1:V2 Use Caching to Reduce Network Load Read (RPC) Return (Data) Write (RPC) ACK Client cache Client cache Advantage: if open/read/write/close can be done locally, don’t need to do any network traffic…fast! Problems: –Failure: »Client caches have data not committed at server –Cache consistency! »Client caches not consistent with server/each other F1:V1 F1:V2 read(f1) write(f1)  V1 read(f1)  V1  OK read(f1)  V1 read(f1)  V2 Crash!

13. 23.13 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Failures What if server crashes? Can client wait until server comes back up and continue as before? –Any data in server memory but not on disk can be lost –Shared state across RPC: What if server crashes after seek? Then, when client does “read”, it will fail –Message retries: suppose server crashes after it does UNIX “rm foo”, but before acknowledgment? »Message system will retry: send it again »How does it know not to delete it again? (could solve with two- phase commit protocol, but NFS takes a more ad hoc approach) Stateless protocol: A protocol in which all information required to process a request is passed with request –Server keeps no state about client, except as hints to help improve performance (e.g. a cache) –Thus, if server crashes and restarted, requests can continue where left off (in many cases) What if client crashes? –Might lose modified data in client cache Crash!

14. 23.14 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Network File System (NFS) Three Layers for NFS system –UNIX file-system interface: open, read, write, close calls + file descriptors –VFS layer: distinguishes local from remote files »Calls the NFS protocol procedures for remote requests –NFS service layer: bottom layer of the architecture »Implements the NFS protocol NFS Protocol: RPC for file operations on server –Reading/searching a directory –Manipulating links and directories –Accessing file attributes/reading and writing files Write-through caching: Modified data committed to server’s disk before results are returned to the client –Lose some of the advantages of caching –Time to perform write() can be long –Need some mechanism for readers to eventually notice changes! (more on this later)

15. 23.15 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 NFS Continued NFS servers are stateless; each request provides all arguments require for execution –E.g. reads include information for entire operation, such as ReadAt(inumber,position), not Read(openfile) –No need to perform network open() or close() on file – each operation stands on its own Idempotent: Performing requests multiple times has same effect as performing it exactly once –Example: Server crashes between disk I/O and message send, client resend read, server does operation again –Example: Read and write file blocks: just re-read or re-write file block – no side effects –Example: What about “remove”? NFS does operation twice and second time returns an advisory error Failure Model: Transparent to client system –Is this a good idea? What if you are in the middle of reading a file and server crashes? –Options (NFS Provides both): »Hang until server comes back up (next week?) »Return an error. (Of course, most applications don’t know they are talking over network)

16. 23.16 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Schematic View of NFS Architecture

17. 23.17 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 NFS protocol: weak consistency –Client polls server periodically to check for changes »Polls server if data hasn’t been checked in last 3-30 seconds (exact timeout it tunable parameter). »Thus, when file is changed on one client, server is notified, but other clients use old version of file until timeout. –What if multiple clients write to same file? »In NFS, can get either version (or parts of both) »Completely arbitrary! cache F1:V2 Server Write (RPC) ACK Client cache Client cache F1:V1 F1:V2 NFS Cache consistency F1 still ok? No: (F1:V2)

18. 23.18 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 NFS Pros and Cons NFS Pros: –Simple, Highly portable NFS Cons: –Sometimes inconsistent! –Doesn’t scale to large # clients »Must keep checking to see if caches out of date »Server becomes bottleneck due to polling traffic

19. 23.19 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Administrivia Updated Project 4 spec and skeleton will be posted by Friday Final Exam Review –Monday 5/6, 2-5pm in 100 Lewis Hall Final Exam –Friday 5/17, 8-11am in 1 Pimentel –All material from the course »With slightly more focus on second half, but you are still responsible for all the material –Two sheets of notes, both sides –Dumb calculator allowed

20. 23.20 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Q1: True _ False _ RPC requires special networking support and functionality Q2: True _ False _ The client and server for RPC must use the same hardware architecture (e.g., little endian) Q3: True _ False _ Local procedure call << same-machine RPC << remote machine RPC Q4: True _ False _ NFS provides weak client-server data consistency Quiz 23.1: RPC and NFS

21. 23.21 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 5min Break

22. 23.22 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Q1: True _ False _ RPC requires special networking support and functionality Q2: True _ False _ The client and server for RPC must use the same hardware architecture (e.g., little endian) Q3: True _ False _ Local procedure call << same-machine RPC << remote machine RPC Q4: True _ False _ NFS provides weak client-server data consistency Quiz 23.1: RPC and NFS X X X X

23. 23.23 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Andrew File System Andrew File System (AFS, late 80’s)  DCE DFS (commercial product) Callbacks: Server records who has copy of file –On changes, server immediately tells all with old copy –No polling bandwidth (continuous checking) needed Write through on close –Changes not propagated to server until close() –Session semantics: updates visible to other clients only after the file is closed »As a result, do not get partial writes: all or nothing! »Although, for processes on local machine, updates visible immediately to other programs who have file open In AFS, everyone who has file open sees old version –Don’t get newer versions until reopen file

24. 23.24 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Andrew File System (con’t) Data cached on local disk of client as well as memory –On open with a cache miss (file not on local disk): »Get file from server, set up callback with server –On write followed by close: »Send copy to server; tells all clients with copies to fetch new version from server on next open (using callbacks) What if server crashes? Lose all callback state! –Reconstruct callback information from client: go ask everyone “who has which files cached?” AFS Pro: Relative to NFS, less server load: –Disk as cache  more files can be cached locally –Callbacks  server not involved if file is read-only For both AFS and NFS: central server is bottleneck! –Performance: all writes  server, cache misses  server –Availability: Server is single point of failure –Cost: server machine’s high cost relative to workstation

25. 23.25 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 World Wide Web Key idea: graphical front-end to RPC protocol What happens when a web server fails? –System breaks! –Solution: Transport or network-layer redirection »Invisible to applications »Can also help with scalability (load balancers) »Must handle “sessions” (e.g., banking/e-commerce) Initial version: no caching –Didn’t scale well – easy to overload servers

26. 23.26 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 WWW Caching Use client-side caching to reduce number of interactions between clients and servers and/or reduce the size of the interactions: –Time-to-Live (TTL) fields – HTTP “Expires” header from server –Client polling – HTTP “If-Modified-Since” request headers from clients –Server refresh – HTML “META Refresh tag” causes periodic client poll What is the polling frequency for clients and servers? –Could be adaptive based upon a page’s age and its rate of change Server load is still significant!

27. 23.27 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 WWW Proxy Caches Place caches in the network to reduce server load –But, increases latency in lightly loaded case –Caches near servers called “reverse proxy caches” »Offloads busy server machines –Caches at the “edges” of the network called “content distribution networks” »Offloads servers and reduce client latency Challenges: –Caching static traffic easy, but only ~40% of traffic –Dynamic and multimedia is harder »Multimedia is a big win: Megabytes versus Kilobytes –Same cache consistency problems as before Caching is changing the Internet architecture –Places functionality at higher levels of comm. protocols

28. 23.28 4/24/2013 Anthony D. Joseph CS162 ©UCB Spring 2013 Conclusion Remote Procedure Call (RPC): Call procedure on remote machine –Provides same interface as procedure –Automatic packing and unpacking of arguments without user programming (in stub) Distributed File System: –Transparent access to files stored on a remote disk »NFS uses caching for performance Cache Consistency: Keeping contents of client caches consistent with one another –If multiple clients, some reading and some writing, how do stale cached copies get updated? –NFS: check periodically for changes WWW: Caching to load balance, reduce latency/costs –Server and edge caches